SSDA shares challenges with both conventional UDA and SSL approaches. Although SSDA can be approached by directly using the labeled target data to supervise the segmentation task together with the supervised source data, doing so may lead to intra-domain discrepancy in the target domain which was demonstrated in semi-supervised domain adaptation for image classification task [1]} where the target domain is separated into two unaligned sub-distributions due to aligned labeled source and labeled target data. That is, enforcing partial alignment between full labeled source data and a few labeled target data does not guarantee that the remaining unlabeled target samples will be attracted toward source feature clusters, thus leaving them unaligned. The presence of the intra-domain discrepancy makes traditional domain adaptation methods less suitable for SSDA.
To address SSDA, Chen et al. [2]} propose a two-step domain mixing model to effectively utilize labeled data in different domains by attempting to extract domain-invariant representations and so reduce the domain gap. In this approach the cross-domain region-level mixing employs CutMix [3]} and so fails to respect object boundaries. Furthermore, cross-domain mixing during domain adaptation doesn't take into account the intra-domain discrepancy within the target domain, which has never been introduced and tackled in SSDA based semantic segmentation.
